Physics without laws - Making exact predictions with data based methods


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lars.kindermann [ at ] awi.de

Abstract

The mathematical method of fractional or continuous iteration can be used to model a dynamical system exactly from limited experimental data. However, mathematics is complicated and exact solutions - even if proven to exist - can rarely be found analytically. We have shown previously that neural networks can be utilized to numerically compute fractional iterates of mathematical functions. In this paper we demonstrate the application of this method to the fundamental experiment of physics: The free fall.



Item Type
Conference (Conference paper)
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Not peer-reviewed
Publication Status
Published
Event Details
Proceedings of the International Joint Conference on Neural Networks (IJCNN'2002), Honolulu, Vol 2.
Eprint ID
10413
DOI 10.1109/IJCNN.2002.1007769

Cite as
Kindermann, L. and Protzel, P. (2002): Physics without laws - Making exact predictions with data based methods , Proceedings of the International Joint Conference on Neural Networks (IJCNN'2002), Honolulu, Vol 2 . doi: 10.1109/IJCNN.2002.1007769


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